import pandas as pd
import time, datetime
df_data_5minute=pd.read_csv('黄金主力5分钟数据.csv')
'''
或者使用JQdata
from jqdatasdk import *
#jqdata的账号密码
auth('邮箱：', 'jiaohiabin@ruc.edu.cn')
df_data_5minute= get_price('AU9999.XSGE', start_date='2016-01-01', end_date='2018-01-01', frequency='5m')
'''
df_data_5minute.head()
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| Unnamed: 0 | open | close | high | low | volume | money | |
|---|---|---|---|---|---|---|---|
| 0 | 2016-01-04 09:05:00 | 226.70 | 226.65 | 226.85 | 226.45 | 5890.0 | 1.335146e+09 |
| 1 | 2016-01-04 09:10:00 | 226.75 | 226.50 | 226.75 | 226.40 | 2562.0 | 5.804133e+08 |
| 2 | 2016-01-04 09:15:00 | 226.45 | 226.45 | 226.60 | 226.40 | 1638.0 | 3.709666e+08 |
| 3 | 2016-01-04 09:20:00 | 226.45 | 226.25 | 226.50 | 226.20 | 3162.0 | 7.157891e+08 |
| 4 | 2016-01-04 09:25:00 | 226.25 | 226.25 | 226.30 | 226.20 | 1684.0 | 3.809907e+08 |
df_data_5minute
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| open | close | high | low | volume | money | |
|---|---|---|---|---|---|---|
| 0 | 226.70 | 226.65 | 226.85 | 226.45 | 5890.0 | 1.335146e+09 |
| 1 | 226.75 | 226.50 | 226.75 | 226.40 | 2562.0 | 5.804133e+08 |
| 2 | 226.45 | 226.45 | 226.60 | 226.40 | 1638.0 | 3.709666e+08 |
| 3 | 226.45 | 226.25 | 226.50 | 226.20 | 3162.0 | 7.157891e+08 |
| 4 | 226.25 | 226.25 | 226.30 | 226.20 | 1684.0 | 3.809907e+08 |
| 5 | 226.25 | 226.30 | 226.35 | 226.20 | 922.0 | 2.086313e+08 |
| 6 | 226.30 | 226.35 | 226.40 | 226.20 | 2476.0 | 5.603541e+08 |
| 7 | 226.30 | 226.45 | 226.45 | 226.25 | 2516.0 | 5.695246e+08 |
| 8 | 226.45 | 226.35 | 226.45 | 226.30 | 1344.0 | 3.042327e+08 |
| 9 | 226.30 | 226.30 | 226.35 | 226.20 | 1414.0 | 3.199363e+08 |
| 10 | 226.35 | 226.45 | 226.50 | 226.30 | 1610.0 | 3.645328e+08 |
| 11 | 226.45 | 226.40 | 226.50 | 226.40 | 972.0 | 2.200957e+08 |
| 12 | 226.40 | 226.50 | 226.55 | 226.35 | 2004.0 | 4.538166e+08 |
| 13 | 226.50 | 226.45 | 226.55 | 226.40 | 780.0 | 1.766423e+08 |
| 14 | 226.45 | 226.45 | 226.50 | 226.40 | 1530.0 | 3.464690e+08 |
| 15 | 226.55 | 226.45 | 226.65 | 226.45 | 2564.0 | 5.807784e+08 |
| 16 | 226.45 | 226.50 | 226.55 | 226.45 | 900.0 | 2.038475e+08 |
| 17 | 226.55 | 226.70 | 226.80 | 226.50 | 3008.0 | 6.817039e+08 |
| 18 | 226.70 | 226.65 | 226.85 | 226.60 | 2510.0 | 5.691306e+08 |
| 19 | 226.65 | 226.60 | 226.65 | 226.60 | 930.0 | 2.107595e+08 |
| 20 | 226.65 | 226.75 | 226.75 | 226.60 | 1184.0 | 2.683818e+08 |
| 21 | 226.75 | 226.65 | 226.75 | 226.60 | 1044.0 | 2.366603e+08 |
| 22 | 226.65 | 226.60 | 226.70 | 226.60 | 342.0 | 7.751130e+07 |
| 23 | 226.60 | 226.60 | 226.65 | 226.55 | 640.0 | 1.450196e+08 |
| 24 | 226.60 | 226.65 | 226.70 | 226.60 | 502.0 | 1.137778e+08 |
| 25 | 226.65 | 226.95 | 226.95 | 226.65 | 3222.0 | 7.308042e+08 |
| 26 | 226.90 | 226.90 | 226.95 | 226.80 | 1472.0 | 3.339398e+08 |
| 27 | 227.10 | 227.25 | 227.25 | 227.00 | 4894.0 | 1.111496e+09 |
| 28 | 227.25 | 227.55 | 227.60 | 227.20 | 5338.0 | 1.214103e+09 |
| 29 | 227.60 | 227.75 | 228.00 | 227.50 | 8612.0 | 1.961599e+09 |
| ... | ... | ... | ... | ... | ... | ... |
| 53280 | 278.05 | 277.95 | 278.05 | 277.90 | 448.0 | 1.245318e+08 |
| 53281 | 277.90 | 277.95 | 278.00 | 277.90 | 506.0 | 1.406423e+08 |
| 53282 | 277.95 | 277.95 | 278.00 | 277.95 | 180.0 | 5.003790e+07 |
| 53283 | 277.95 | 278.00 | 278.05 | 277.95 | 936.0 | 2.602273e+08 |
| 53284 | 278.05 | 277.90 | 278.05 | 277.90 | 942.0 | 2.618281e+08 |
| 53285 | 277.85 | 277.90 | 277.95 | 277.85 | 518.0 | 1.439454e+08 |
| 53286 | 277.95 | 277.95 | 277.95 | 277.90 | 614.0 | 1.706443e+08 |
| 53287 | 277.90 | 277.90 | 277.95 | 277.85 | 1046.0 | 2.906776e+08 |
| 53288 | 277.95 | 277.90 | 277.95 | 277.90 | 206.0 | 5.725350e+07 |
| 53289 | 277.90 | 277.90 | 277.95 | 277.85 | 740.0 | 2.056435e+08 |
| 53290 | 277.90 | 277.85 | 277.90 | 277.85 | 200.0 | 5.557570e+07 |
| 53291 | 277.90 | 277.90 | 277.95 | 277.85 | 756.0 | 2.100840e+08 |
| 53292 | 277.90 | 278.00 | 278.00 | 277.90 | 490.0 | 1.362097e+08 |
| 53293 | 278.00 | 278.05 | 278.15 | 278.00 | 768.0 | 2.135675e+08 |
| 53294 | 278.10 | 278.15 | 278.15 | 278.05 | 252.0 | 7.008070e+07 |
| 53295 | 278.10 | 278.05 | 278.10 | 278.00 | 800.0 | 2.224430e+08 |
| 53296 | 278.00 | 278.00 | 278.05 | 277.95 | 184.0 | 5.115390e+07 |
| 53297 | 278.00 | 277.95 | 278.00 | 277.90 | 474.0 | 1.317464e+08 |
| 53298 | 277.95 | 277.95 | 277.95 | 277.90 | 334.0 | 9.282880e+07 |
| 53299 | 277.95 | 277.90 | 277.95 | 277.90 | 332.0 | 9.226560e+07 |
| 53300 | 277.90 | 277.95 | 277.95 | 277.90 | 672.0 | 1.867720e+08 |
| 53301 | 277.90 | 277.85 | 277.95 | 277.85 | 994.0 | 2.762458e+08 |
| 53302 | 277.90 | 277.90 | 277.95 | 277.85 | 352.0 | 9.781830e+07 |
| 53303 | 277.90 | 277.80 | 277.95 | 277.80 | 784.0 | 2.178426e+08 |
| 53304 | 277.85 | 277.80 | 277.85 | 277.75 | 920.0 | 2.555711e+08 |
| 53305 | 277.80 | 277.80 | 277.85 | 277.75 | 606.0 | 1.683349e+08 |
| 53306 | 277.80 | 277.85 | 277.85 | 277.80 | 560.0 | 1.555840e+08 |
| 53307 | 277.85 | 277.85 | 277.90 | 277.80 | 802.0 | 2.228271e+08 |
| 53308 | 277.85 | 277.75 | 277.90 | 277.75 | 1236.0 | 3.433855e+08 |
| 53309 | 277.80 | 277.80 | 277.90 | 277.70 | 1790.0 | 4.972797e+08 |
53310 rows × 6 columns
df_data_5minute.drop('Unnamed: 0', axis=1, inplace=True)
df_data_5minute
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| open | close | high | low | volume | money | |
|---|---|---|---|---|---|---|
| 0 | 226.70 | 226.65 | 226.85 | 226.45 | 5890.0 | 1.335146e+09 |
| 1 | 226.75 | 226.50 | 226.75 | 226.40 | 2562.0 | 5.804133e+08 |
| 2 | 226.45 | 226.45 | 226.60 | 226.40 | 1638.0 | 3.709666e+08 |
| 3 | 226.45 | 226.25 | 226.50 | 226.20 | 3162.0 | 7.157891e+08 |
| 4 | 226.25 | 226.25 | 226.30 | 226.20 | 1684.0 | 3.809907e+08 |
| 5 | 226.25 | 226.30 | 226.35 | 226.20 | 922.0 | 2.086313e+08 |
| 6 | 226.30 | 226.35 | 226.40 | 226.20 | 2476.0 | 5.603541e+08 |
| 7 | 226.30 | 226.45 | 226.45 | 226.25 | 2516.0 | 5.695246e+08 |
| 8 | 226.45 | 226.35 | 226.45 | 226.30 | 1344.0 | 3.042327e+08 |
| 9 | 226.30 | 226.30 | 226.35 | 226.20 | 1414.0 | 3.199363e+08 |
| 10 | 226.35 | 226.45 | 226.50 | 226.30 | 1610.0 | 3.645328e+08 |
| 11 | 226.45 | 226.40 | 226.50 | 226.40 | 972.0 | 2.200957e+08 |
| 12 | 226.40 | 226.50 | 226.55 | 226.35 | 2004.0 | 4.538166e+08 |
| 13 | 226.50 | 226.45 | 226.55 | 226.40 | 780.0 | 1.766423e+08 |
| 14 | 226.45 | 226.45 | 226.50 | 226.40 | 1530.0 | 3.464690e+08 |
| 15 | 226.55 | 226.45 | 226.65 | 226.45 | 2564.0 | 5.807784e+08 |
| 16 | 226.45 | 226.50 | 226.55 | 226.45 | 900.0 | 2.038475e+08 |
| 17 | 226.55 | 226.70 | 226.80 | 226.50 | 3008.0 | 6.817039e+08 |
| 18 | 226.70 | 226.65 | 226.85 | 226.60 | 2510.0 | 5.691306e+08 |
| 19 | 226.65 | 226.60 | 226.65 | 226.60 | 930.0 | 2.107595e+08 |
| 20 | 226.65 | 226.75 | 226.75 | 226.60 | 1184.0 | 2.683818e+08 |
| 21 | 226.75 | 226.65 | 226.75 | 226.60 | 1044.0 | 2.366603e+08 |
| 22 | 226.65 | 226.60 | 226.70 | 226.60 | 342.0 | 7.751130e+07 |
| 23 | 226.60 | 226.60 | 226.65 | 226.55 | 640.0 | 1.450196e+08 |
| 24 | 226.60 | 226.65 | 226.70 | 226.60 | 502.0 | 1.137778e+08 |
| 25 | 226.65 | 226.95 | 226.95 | 226.65 | 3222.0 | 7.308042e+08 |
| 26 | 226.90 | 226.90 | 226.95 | 226.80 | 1472.0 | 3.339398e+08 |
| 27 | 227.10 | 227.25 | 227.25 | 227.00 | 4894.0 | 1.111496e+09 |
| 28 | 227.25 | 227.55 | 227.60 | 227.20 | 5338.0 | 1.214103e+09 |
| 29 | 227.60 | 227.75 | 228.00 | 227.50 | 8612.0 | 1.961599e+09 |
| ... | ... | ... | ... | ... | ... | ... |
| 53280 | 278.05 | 277.95 | 278.05 | 277.90 | 448.0 | 1.245318e+08 |
| 53281 | 277.90 | 277.95 | 278.00 | 277.90 | 506.0 | 1.406423e+08 |
| 53282 | 277.95 | 277.95 | 278.00 | 277.95 | 180.0 | 5.003790e+07 |
| 53283 | 277.95 | 278.00 | 278.05 | 277.95 | 936.0 | 2.602273e+08 |
| 53284 | 278.05 | 277.90 | 278.05 | 277.90 | 942.0 | 2.618281e+08 |
| 53285 | 277.85 | 277.90 | 277.95 | 277.85 | 518.0 | 1.439454e+08 |
| 53286 | 277.95 | 277.95 | 277.95 | 277.90 | 614.0 | 1.706443e+08 |
| 53287 | 277.90 | 277.90 | 277.95 | 277.85 | 1046.0 | 2.906776e+08 |
| 53288 | 277.95 | 277.90 | 277.95 | 277.90 | 206.0 | 5.725350e+07 |
| 53289 | 277.90 | 277.90 | 277.95 | 277.85 | 740.0 | 2.056435e+08 |
| 53290 | 277.90 | 277.85 | 277.90 | 277.85 | 200.0 | 5.557570e+07 |
| 53291 | 277.90 | 277.90 | 277.95 | 277.85 | 756.0 | 2.100840e+08 |
| 53292 | 277.90 | 278.00 | 278.00 | 277.90 | 490.0 | 1.362097e+08 |
| 53293 | 278.00 | 278.05 | 278.15 | 278.00 | 768.0 | 2.135675e+08 |
| 53294 | 278.10 | 278.15 | 278.15 | 278.05 | 252.0 | 7.008070e+07 |
| 53295 | 278.10 | 278.05 | 278.10 | 278.00 | 800.0 | 2.224430e+08 |
| 53296 | 278.00 | 278.00 | 278.05 | 277.95 | 184.0 | 5.115390e+07 |
| 53297 | 278.00 | 277.95 | 278.00 | 277.90 | 474.0 | 1.317464e+08 |
| 53298 | 277.95 | 277.95 | 277.95 | 277.90 | 334.0 | 9.282880e+07 |
| 53299 | 277.95 | 277.90 | 277.95 | 277.90 | 332.0 | 9.226560e+07 |
| 53300 | 277.90 | 277.95 | 277.95 | 277.90 | 672.0 | 1.867720e+08 |
| 53301 | 277.90 | 277.85 | 277.95 | 277.85 | 994.0 | 2.762458e+08 |
| 53302 | 277.90 | 277.90 | 277.95 | 277.85 | 352.0 | 9.781830e+07 |
| 53303 | 277.90 | 277.80 | 277.95 | 277.80 | 784.0 | 2.178426e+08 |
| 53304 | 277.85 | 277.80 | 277.85 | 277.75 | 920.0 | 2.555711e+08 |
| 53305 | 277.80 | 277.80 | 277.85 | 277.75 | 606.0 | 1.683349e+08 |
| 53306 | 277.80 | 277.85 | 277.85 | 277.80 | 560.0 | 1.555840e+08 |
| 53307 | 277.85 | 277.85 | 277.90 | 277.80 | 802.0 | 2.228271e+08 |
| 53308 | 277.85 | 277.75 | 277.90 | 277.75 | 1236.0 | 3.433855e+08 |
| 53309 | 277.80 | 277.80 | 277.90 | 277.70 | 1790.0 | 4.972797e+08 |
53310 rows × 6 columns
df=df_data_5minute
close = df['close']
df.drop(labels=['close'], axis=1,inplace = True)
df.insert(0, 'close', close)
df
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| close | open | high | low | volume | money | |
|---|---|---|---|---|---|---|
| 0 | 226.65 | 226.70 | 226.85 | 226.45 | 5890.0 | 1.335146e+09 |
| 1 | 226.50 | 226.75 | 226.75 | 226.40 | 2562.0 | 5.804133e+08 |
| 2 | 226.45 | 226.45 | 226.60 | 226.40 | 1638.0 | 3.709666e+08 |
| 3 | 226.25 | 226.45 | 226.50 | 226.20 | 3162.0 | 7.157891e+08 |
| 4 | 226.25 | 226.25 | 226.30 | 226.20 | 1684.0 | 3.809907e+08 |
| 5 | 226.30 | 226.25 | 226.35 | 226.20 | 922.0 | 2.086313e+08 |
| 6 | 226.35 | 226.30 | 226.40 | 226.20 | 2476.0 | 5.603541e+08 |
| 7 | 226.45 | 226.30 | 226.45 | 226.25 | 2516.0 | 5.695246e+08 |
| 8 | 226.35 | 226.45 | 226.45 | 226.30 | 1344.0 | 3.042327e+08 |
| 9 | 226.30 | 226.30 | 226.35 | 226.20 | 1414.0 | 3.199363e+08 |
| 10 | 226.45 | 226.35 | 226.50 | 226.30 | 1610.0 | 3.645328e+08 |
| 11 | 226.40 | 226.45 | 226.50 | 226.40 | 972.0 | 2.200957e+08 |
| 12 | 226.50 | 226.40 | 226.55 | 226.35 | 2004.0 | 4.538166e+08 |
| 13 | 226.45 | 226.50 | 226.55 | 226.40 | 780.0 | 1.766423e+08 |
| 14 | 226.45 | 226.45 | 226.50 | 226.40 | 1530.0 | 3.464690e+08 |
| 15 | 226.45 | 226.55 | 226.65 | 226.45 | 2564.0 | 5.807784e+08 |
| 16 | 226.50 | 226.45 | 226.55 | 226.45 | 900.0 | 2.038475e+08 |
| 17 | 226.70 | 226.55 | 226.80 | 226.50 | 3008.0 | 6.817039e+08 |
| 18 | 226.65 | 226.70 | 226.85 | 226.60 | 2510.0 | 5.691306e+08 |
| 19 | 226.60 | 226.65 | 226.65 | 226.60 | 930.0 | 2.107595e+08 |
| 20 | 226.75 | 226.65 | 226.75 | 226.60 | 1184.0 | 2.683818e+08 |
| 21 | 226.65 | 226.75 | 226.75 | 226.60 | 1044.0 | 2.366603e+08 |
| 22 | 226.60 | 226.65 | 226.70 | 226.60 | 342.0 | 7.751130e+07 |
| 23 | 226.60 | 226.60 | 226.65 | 226.55 | 640.0 | 1.450196e+08 |
| 24 | 226.65 | 226.60 | 226.70 | 226.60 | 502.0 | 1.137778e+08 |
| 25 | 226.95 | 226.65 | 226.95 | 226.65 | 3222.0 | 7.308042e+08 |
| 26 | 226.90 | 226.90 | 226.95 | 226.80 | 1472.0 | 3.339398e+08 |
| 27 | 227.25 | 227.10 | 227.25 | 227.00 | 4894.0 | 1.111496e+09 |
| 28 | 227.55 | 227.25 | 227.60 | 227.20 | 5338.0 | 1.214103e+09 |
| 29 | 227.75 | 227.60 | 228.00 | 227.50 | 8612.0 | 1.961599e+09 |
| ... | ... | ... | ... | ... | ... | ... |
| 53280 | 277.95 | 278.05 | 278.05 | 277.90 | 448.0 | 1.245318e+08 |
| 53281 | 277.95 | 277.90 | 278.00 | 277.90 | 506.0 | 1.406423e+08 |
| 53282 | 277.95 | 277.95 | 278.00 | 277.95 | 180.0 | 5.003790e+07 |
| 53283 | 278.00 | 277.95 | 278.05 | 277.95 | 936.0 | 2.602273e+08 |
| 53284 | 277.90 | 278.05 | 278.05 | 277.90 | 942.0 | 2.618281e+08 |
| 53285 | 277.90 | 277.85 | 277.95 | 277.85 | 518.0 | 1.439454e+08 |
| 53286 | 277.95 | 277.95 | 277.95 | 277.90 | 614.0 | 1.706443e+08 |
| 53287 | 277.90 | 277.90 | 277.95 | 277.85 | 1046.0 | 2.906776e+08 |
| 53288 | 277.90 | 277.95 | 277.95 | 277.90 | 206.0 | 5.725350e+07 |
| 53289 | 277.90 | 277.90 | 277.95 | 277.85 | 740.0 | 2.056435e+08 |
| 53290 | 277.85 | 277.90 | 277.90 | 277.85 | 200.0 | 5.557570e+07 |
| 53291 | 277.90 | 277.90 | 277.95 | 277.85 | 756.0 | 2.100840e+08 |
| 53292 | 278.00 | 277.90 | 278.00 | 277.90 | 490.0 | 1.362097e+08 |
| 53293 | 278.05 | 278.00 | 278.15 | 278.00 | 768.0 | 2.135675e+08 |
| 53294 | 278.15 | 278.10 | 278.15 | 278.05 | 252.0 | 7.008070e+07 |
| 53295 | 278.05 | 278.10 | 278.10 | 278.00 | 800.0 | 2.224430e+08 |
| 53296 | 278.00 | 278.00 | 278.05 | 277.95 | 184.0 | 5.115390e+07 |
| 53297 | 277.95 | 278.00 | 278.00 | 277.90 | 474.0 | 1.317464e+08 |
| 53298 | 277.95 | 277.95 | 277.95 | 277.90 | 334.0 | 9.282880e+07 |
| 53299 | 277.90 | 277.95 | 277.95 | 277.90 | 332.0 | 9.226560e+07 |
| 53300 | 277.95 | 277.90 | 277.95 | 277.90 | 672.0 | 1.867720e+08 |
| 53301 | 277.85 | 277.90 | 277.95 | 277.85 | 994.0 | 2.762458e+08 |
| 53302 | 277.90 | 277.90 | 277.95 | 277.85 | 352.0 | 9.781830e+07 |
| 53303 | 277.80 | 277.90 | 277.95 | 277.80 | 784.0 | 2.178426e+08 |
| 53304 | 277.80 | 277.85 | 277.85 | 277.75 | 920.0 | 2.555711e+08 |
| 53305 | 277.80 | 277.80 | 277.85 | 277.75 | 606.0 | 1.683349e+08 |
| 53306 | 277.85 | 277.80 | 277.85 | 277.80 | 560.0 | 1.555840e+08 |
| 53307 | 277.85 | 277.85 | 277.90 | 277.80 | 802.0 | 2.228271e+08 |
| 53308 | 277.75 | 277.85 | 277.90 | 277.75 | 1236.0 | 3.433855e+08 |
| 53309 | 277.80 | 277.80 | 277.90 | 277.70 | 1790.0 | 4.972797e+08 |
53310 rows × 6 columns
data_train =df.iloc[:int(df.shape[0] * 0.7), :]
data_test = df.iloc[int(df.shape[0] * 0.7):, :]
print(data_train.shape, data_test.shape)
(37317, 6) (15993, 6)
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.preprocessing import MinMaxScaler
import time
scaler = MinMaxScaler(feature_range=(-1, 1))
scaler.fit(data_train)
MinMaxScaler(copy=True, feature_range=(-1, 1))
data_train = scaler.transform(data_train)
data_test = scaler.transform(data_test)
data_train
array([[-0.98877193, -0.98736842, -0.98459384, -0.99297259, -0.82504604,
-0.85978547],
[-0.99298246, -0.98596491, -0.98739496, -0.99437807, -0.92389948,
-0.93904608],
[-0.99438596, -0.99438596, -0.99159664, -0.99437807, -0.95134557,
-0.96104178],
...,
[ 0.61263158, 0.61824561, 0.61484594, 0.61349262, -0.90916652,
-0.90885626],
[ 0.61684211, 0.61403509, 0.61204482, 0.61630358, -0.94754352,
-0.94737162],
[ 0.6154386 , 0.6154386 , 0.61064426, 0.61349262, -0.94445435,
-0.9442865 ]])
from keras.layers import Input, Dense, LSTM
from keras.models import Model
from keras.layers import *
from keras.models import *
from keras.optimizers import Adam
output_dim = 1
batch_size = 256
epochs = 60
seq_len = 5
hidden_size = 128
TIME_STEPS = 5
INPUT_DIM = 6
lstm_units = 64
X_train = np.array([data_train[i : i + seq_len, :] for i in range(data_train.shape[0] - seq_len)])
y_train = np.array([data_train[i + seq_len, 0] for i in range(data_train.shape[0]- seq_len)])
X_test = np.array([data_test[i : i + seq_len, :] for i in range(data_test.shape[0]- seq_len)])
y_test = np.array([data_test[i + seq_len, 0] for i in range(data_test.shape[0] - seq_len)])
print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)
(37312, 5, 6) (37312,) (15988, 5, 6) (15988,)
inputs = Input(shape=(TIME_STEPS, INPUT_DIM))
#drop1 = Dropout(0.3)(inputs)
x = Conv1D(filters = 64, kernel_size = 1, activation = 'relu')(inputs) #, padding = 'same'
#x = Conv1D(filters=128, kernel_size=5, activation='relu')(output1)#embedded_sequences
x = MaxPooling1D(pool_size = 5)(x)
x = Dropout(0.2)(x)
print(x.shape)
(?, 1, 64)
lstm_out = Bidirectional(LSTM(lstm_units, activation='relu'), name='bilstm')(x)
#lstm_out = LSTM(lstm_units,activation='relu')(x)
print(lstm_out.shape)
(?, 128)
output = Dense(1, activation='sigmoid')(lstm_out)
#output = Dense(10, activation='sigmoid')(drop2)
model = Model(inputs=inputs, outputs=output)
print(model.summary())
_________________________________________________________________ Layer (type) Output Shape Param # ================================================================= input_7 (InputLayer) (None, 5, 6) 0 _________________________________________________________________ conv1d_7 (Conv1D) (None, 5, 64) 448 _________________________________________________________________ max_pooling1d_7 (MaxPooling1 (None, 1, 64) 0 _________________________________________________________________ dropout_10 (Dropout) (None, 1, 64) 0 _________________________________________________________________ bilstm (Bidirectional) (None, 128) 66048 _________________________________________________________________ dense_6 (Dense) (None, 1) 129 ================================================================= Total params: 66,625 Trainable params: 66,625 Non-trainable params: 0 _________________________________________________________________ None
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, shuffle=False)
y_pred = model.predict(X_test)
print('MSE Train loss:', model.evaluate(X_train, y_train, batch_size=batch_size))
print('MSE Test loss:', model.evaluate(X_test, y_test, batch_size=batch_size))
plt.plot(y_test, label='test')
plt.plot(y_pred, label='pred')
plt.legend()
plt.show()
Epoch 1/60 37312/37312 [==============================] - 4s 106us/step - loss: 0.1970 Epoch 2/60 37312/37312 [==============================] - 2s 43us/step - loss: 0.0618 Epoch 3/60 37312/37312 [==============================] - 2s 45us/step - loss: 0.0438 Epoch 4/60 37312/37312 [==============================] - 2s 48us/step - loss: 0.0434 Epoch 5/60 37312/37312 [==============================] - 1s 40us/step - loss: 0.0432 Epoch 6/60 37312/37312 [==============================] - 2s 43us/step - loss: 0.0429 Epoch 7/60 37312/37312 [==============================] - 1s 35us/step - loss: 0.0427 Epoch 8/60 37312/37312 [==============================] - 1s 33us/step - loss: 0.0425 Epoch 9/60 37312/37312 [==============================] - 1s 33us/step - loss: 0.0424 Epoch 10/60 37312/37312 [==============================] - 2s 49us/step - loss: 0.0422 Epoch 11/60 37312/37312 [==============================] - 1s 34us/step - loss: 0.0421 Epoch 12/60 37312/37312 [==============================] - 1s 32us/step - loss: 0.0419 Epoch 13/60 37312/37312 [==============================] - 1s 36us/step - loss: 0.0418 Epoch 14/60 37312/37312 [==============================] - 1s 37us/step - loss: 0.0417 Epoch 15/60 37312/37312 [==============================] - 1s 32us/step - loss: 0.0417 Epoch 16/60 37312/37312 [==============================] - 1s 32us/step - loss: 0.0416 Epoch 17/60 37312/37312 [==============================] - 1s 37us/step - loss: 0.0416 Epoch 18/60 37312/37312 [==============================] - 1s 36us/step - loss: 0.0415 Epoch 19/60 37312/37312 [==============================] - 1s 32us/step - loss: 0.0414 Epoch 20/60 37312/37312 [==============================] - 1s 32us/step - loss: 0.0414 Epoch 21/60 37312/37312 [==============================] - 1s 38us/step - loss: 0.0414 Epoch 22/60 37312/37312 [==============================] - 1s 36us/step - loss: 0.0414 Epoch 23/60 37312/37312 [==============================] - 1s 32us/step - loss: 0.0414 Epoch 24/60 37312/37312 [==============================] - 1s 32us/step - loss: 0.0414 Epoch 25/60 37312/37312 [==============================] - 1s 38us/step - loss: 0.0414 Epoch 26/60 37312/37312 [==============================] - 1s 37us/step - loss: 0.0414 Epoch 27/60 37312/37312 [==============================] - 1s 33us/step - loss: 0.0414 Epoch 28/60 37312/37312 [==============================] - 1s 36us/step - loss: 0.0414 Epoch 29/60 37312/37312 [==============================] - 1s 38us/step - loss: 0.0414 Epoch 30/60 37312/37312 [==============================] - 1s 35us/step - loss: 0.0413 Epoch 31/60 37312/37312 [==============================] - 1s 33us/step - loss: 0.0413 Epoch 32/60 37312/37312 [==============================] - 1s 36us/step - loss: 0.0413 Epoch 33/60 37312/37312 [==============================] - 2s 44us/step - loss: 0.0413 Epoch 34/60 37312/37312 [==============================] - 2s 41us/step - loss: 0.0413 Epoch 35/60 37312/37312 [==============================] - 1s 34us/step - loss: 0.0414 Epoch 36/60 37312/37312 [==============================] - 1s 39us/step - loss: 0.0413 Epoch 37/60 37312/37312 [==============================] - 1s 36us/step - loss: 0.0412 Epoch 38/60 37312/37312 [==============================] - 1s 32us/step - loss: 0.0412 Epoch 39/60 37312/37312 [==============================] - 1s 35us/step - loss: 0.0412 Epoch 40/60 37312/37312 [==============================] - 1s 39us/step - loss: 0.0412 Epoch 41/60 37312/37312 [==============================] - 1s 34us/step - loss: 0.0413 Epoch 42/60 37312/37312 [==============================] - 1s 33us/step - loss: 0.0412 Epoch 43/60 37312/37312 [==============================] - 1s 37us/step - loss: 0.0412 Epoch 44/60 37312/37312 [==============================] - 1s 38us/step - loss: 0.0412 Epoch 45/60 37312/37312 [==============================] - 1s 33us/step - loss: 0.0412 Epoch 46/60 37312/37312 [==============================] - 1s 34us/step - loss: 0.0411 Epoch 47/60 37312/37312 [==============================] - 1s 39us/step - loss: 0.0412 Epoch 48/60 37312/37312 [==============================] - 1s 38us/step - loss: 0.0411 Epoch 49/60 37312/37312 [==============================] - 1s 33us/step - loss: 0.0412 Epoch 50/60 37312/37312 [==============================] - 1s 37us/step - loss: 0.0411 Epoch 51/60 37312/37312 [==============================] - 2s 43us/step - loss: 0.0411 Epoch 52/60 37312/37312 [==============================] - 1s 37us/step - loss: 0.0411 Epoch 53/60 37312/37312 [==============================] - 1s 33us/step - loss: 0.0412 Epoch 54/60 37312/37312 [==============================] - 1s 37us/step - loss: 0.0410 Epoch 55/60 37312/37312 [==============================] - 1s 39us/step - loss: 0.0411 Epoch 56/60 37312/37312 [==============================] - 1s 34us/step - loss: 0.0411 Epoch 57/60 37312/37312 [==============================] - 1s 33us/step - loss: 0.0411 Epoch 58/60 37312/37312 [==============================] - 2s 47us/step - loss: 0.0410 Epoch 59/60 37312/37312 [==============================] - 2s 41us/step - loss: 0.0411 Epoch 60/60 37312/37312 [==============================] - 1s 35us/step - loss: 0.0410 37312/37312 [==============================] - 1s 24us/step MSE Train loss: 0.041352386607933875 15988/15988 [==============================] - 0s 15us/step MSE Test loss: 0.0003156892797136216
随着训轮数（epoch）的增加，误差（loss）不断减小 loss: 0.0410左右
